Gailing Hu

Dr. Gailing Hu

orcid

Research Focus & Metrics

Academic Career Length

9Years (2018-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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27 February 2018 in Applied Sciences
Verified
Journal Article
Open access
PDF file
GWGangfeng Wang
Gangfeng Wang
YHYongbiao Hu
Yongbiao Hu
XTXitian Tian
Xitian Tian
JGJunhao Geng
Junhao Geng
GHGailing Hu
Gailing Hu
MZMin Zhang
Min Zhang
Process innovation plays a vital role in the manufacture realization of increasingly complex new products, especially in the context of sustainable development and cleaner production. Knowledge-based innovation design can inspire designers’ creative thinking; however, the existing scattered knowledge has not yet been properly captured and organized according to Computer-Aided Process Innovation (CAPI). Therefore, this paper proposes an integrated approach to tackle this non-trivial issue. By analyzing the design process of CAPI and technical features of open innovation, a novel holistic paradigm of process innovation knowledge capture based on collective intelligence (PIKC-CI) is constructed from the perspective of the knowledge life cycle. Then, a multi-source innovation knowledge fusion algorithm based on semantic elements reconfiguration is applied to form new public knowledge. To ensure the credibility and orderliness of innovation knowledge refinement, a collaborative editing strategy based on knowledge lock and knowledge–social trust degree is explored. Finally, a knowledge management system MPI-OKCS integrating the proposed techniques is implemented into the pre-built CAPI general platform, and a welding process innovation example is provided to illustrate the feasibility of the proposed approach. It is expected that our work would lay the foundation for the future knowledge-inspired CAPI and smart process planning.

16 January 2019 in Micromachines
Verified
Journal Article
Open access
PDF file
GHGailing Hu
Gailing Hu
XZXiang Zhou
Xiang Zhou
GZGuanliang Zhang
Guanliang Zhang
CZChunwei Zhang
Chunwei Zhang
DLDong Li
Dong Li
GWGangfeng Wang
Gangfeng Wang
In traditional laser-based 3D measurement technology, the width of the laser stripe is uncontrollable and uneven. In addition, speckle noise in the image and the noise caused by mechanical movement may reduce the accuracy of the scanning results. This work proposes a new multiple laser stripe scanning profilometry (MLSSP) based on microelectromechanical systems (MEMS) scanning mirror which can project high quality movable laser stripe. It can implement full-field scanning in a short time and does not need to move the measured object or camera. Compared with the traditional laser stripe, the brightness, width and position of the new multiple laser stripes projected by MEMS scanning mirror can be controlled by programming. In addition, the new laser strip can generate high-quality images and the noise caused by mechanical movement is completely eliminated. The experimental results show that the speckle noise is less and the light intensity distribution is more even. Furthermore, the number of pictures needed to be captured is significantly reduced to 1 / N ( N is the number of multiple laser stripes projected by MEMS scanning mirror) and the measurement efficiency is increased by N times, improving the efficiency and accuracy of 3D measurement.